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The term "bias" and "prejudice" even outside statistics are defined by the dictionary as being about getting the wrong results: "preconceived opinion that is no

The term "bias" and "prejudice" even outside statistics are defined by the dictionary as being about getting the wrong results: "preconceived opinion that is not based on reason or actual experience". This agrees more or less with the statistical term, it's just less precise. That's also NOT what machine learning algorithms do. If you want to declare an algorithm biased because it optimizes what it was designed to optimize rather than some random tangential goal, then the term has become meaningless. Similarly, my cell phone is broken because it can only make calls and is ineffective at pulling trains. A locomotive is also broken because it doesn't make tacos. It seems that what you're arguing is that the algorithm shouldn't be blamed in these cases, but in addition, you're declaring that there's no such thing as prejudice or bias...what the algorithms are doing is pulling some (uncomfortable for gatekeepers) platonic truth out of the ether (without any acknowledgement of the possibility of biased input.) I have no idea how you could possibly think I made this claim given that I explicitly listed two examples of algorithms which are biased. I then hinted at how algorithms can eliminate this bias. Consider rereading what I wrote. If you want more details on how algorithms correct biased inputs (it's not based on "ether") read the blog post I linked to downthread: https://www.chrisstucchio.com/blog/2016/alien_intelligences_and_discriminatory_algorithms.html https://www.chrisstucchio.com/blog/2016/alien_intelligences_... Or more simply, you're using this article as an excuse to push a favorite "race realist" agenda which you use to justify a purist libertarianism (and which I agree it requires.) I have no idea why you think "race realist" agendas require purist libertarianism, or vice versa. That's a random political tangent and totally unrelated to this conversation. I'm beginning to think you are seeking to derail the conversation rather than discussing statistics in good faith. In the hopes that I've misunderstood you, I will nevertheless provide you with a good faith clarification. I made no normative claims regarding libertarianism or anything else. The only normative claim I've made is that we should not wrongfully apply an existing term (bias) to completely unrelated concepts (having undesired social outcomes).

throwaway729 10y ago
> That's also NOT what machine learning algorithms do. A machine learning algorithm run on non-representative input could be said to lack experience with the portion of input that isn't represented in the training set. E.g. I think it's pretty obvious that machine learning algorithms exhibit far more vernacular-bias than purely logical reasoning techniques. > I made this claim given that I explicitly listed two examples of algorithms which are biased. Your parent is pointing out the very real possibility that technically-unbiased algorithms can produce vernacularly-biased outputs. I.e., that statistic's definition of "bias" does not sufficiently capture the notion of bias as it's used in the vernacular. I don't think you've refuted that claim. At the end of the day, your approach toward side-stepping the issue of vernacular bias is intellectually lazy. Instead of tackling the problem head on, you're hiding behind a mathematical object that happens to have the same name as the actual thing under discussion. > I'm beginning to think you are seeking to derail the conversation rather than discussing statistics in good faith. I think your characterization of bias in terms of statistic's technical definition is already skimming the edges of good faith. You know what the reporter means when they say "bias", and that's not the meaning that statisticians use in technical settings.
- yummyfajitas 10y ago
  I know what the reporter means: "bias" refers to "outcomes the reporter dislikes and thinks he can generate clickbait with". I'm explicitly advocating against this practice and in favor of using clear and precise language instead. At the end of the day, your approach toward side-stepping the issue of vernacular bias is intellectually lazy. Instead of tackling the problem head on, you're hiding behind a mathematical object that happens to have the same name as the actual thing under discussion. I'm not side stepping the issue. I'm advocating in favor of describing the problem with clear language - once that's done we can begin useful discussion on how to address it. In precise terms, what do you think the problem is?
  - smallnamespace 10y ago
    Didn't he make that clear? > Your parent is pointing out the very real possibility that _technically-unbiased algorithms can produce vernacularly-biased outputs_. I.e., that statistic's definition of "bias" does not sufficiently capture the notion of bias as it's used in the vernacular.
  - throwaway729 10y ago
    > "bias" refers to "outcomes the reporter dislikes and thinks he can generate clickbait with" That's quite a straw man for someone who claims others are trying to derail a conversation. > I'm explicitly advocating against this practice and in favor of using clear and precise language instead. The language you're suggesting masks the problem. It's nice that statisticians came up with a mathematical object that happens to have a politically charged name, but the conflation you're making is not an argument, and doesn't address the underlying (inherently political) problem. > In precise terms, what do you think the problem is? The precise problem is that things like skin color etc. often have strong predictive power. We've recognized this unfortunate fact -- and its societal implications -- and decided to create laws and social norms that regulate (either restrict or sometimes even require) the use of these properties in certain high-impact decisions. Unfortunately, algorithms sometimes use these features to make decisions in settings where a typical human wouldn't (or might even get into legal hot water if they did). Notice that an algorithm can exhibit this behavior without being (statistically) biased. Now, perhaps you think this problem isn't actually a problem. And you can certainly make the argument that any form of vernacular-discrimination should be OK as long as it doesn't exhibit statistical bias. But that's a separate conversation, and you can't defend that position by playing with definitions. And if you're not making that argument, then it should be clear why vernacular-bias differs from statistical-bias.

throwaway729 10y ago

> That's also NOT what machine learning algorithms do. A machine learning algorithm run on non-representative input could be said to lack experience with the portion of input that isn't represented in the training set. E.g. I think it's pretty obvious that machine learning algorithms exhibit far more vernacular-bias than purely logical reasoning techniques. > I made this claim given that I explicitly listed two examples of algorithms which are biased. Your parent is pointing out the very real possibility that technically-unbiased algorithms can produce vernacularly-biased outputs. I.e., that statistic's definition of "bias" does not sufficiently capture the notion of bias as it's used in the vernacular. I don't think you've refuted that claim. At the end of the day, your approach toward side-stepping the issue of vernacular bias is intellectually lazy. Instead of tackling the problem head on, you're hiding behind a mathematical object that happens to have the same name as the actual thing under discussion. > I'm beginning to think you are seeking to derail the conversation rather than discussing statistics in good faith. I think your characterization of bias in terms of statistic's technical definition is already skimming the edges of good faith. You know what the reporter means when they say "bias", and that's not the meaning that statisticians use in technical settings.

yummyfajitas 10y ago
I know what the reporter means: "bias" refers to "outcomes the reporter dislikes and thinks he can generate clickbait with". I'm explicitly advocating against this practice and in favor of using clear and precise language instead. At the end of the day, your approach toward side-stepping the issue of vernacular bias is intellectually lazy. Instead of tackling the problem head on, you're hiding behind a mathematical object that happens to have the same name as the actual thing under discussion. I'm not side stepping the issue. I'm advocating in favor of describing the problem with clear language - once that's done we can begin useful discussion on how to address it. In precise terms, what do you think the problem is?
- smallnamespace 10y ago
  Didn't he make that clear? > Your parent is pointing out the very real possibility that _technically-unbiased algorithms can produce vernacularly-biased outputs_. I.e., that statistic's definition of "bias" does not sufficiently capture the notion of bias as it's used in the vernacular.
- throwaway729 10y ago
  > "bias" refers to "outcomes the reporter dislikes and thinks he can generate clickbait with" That's quite a straw man for someone who claims others are trying to derail a conversation. > I'm explicitly advocating against this practice and in favor of using clear and precise language instead. The language you're suggesting masks the problem. It's nice that statisticians came up with a mathematical object that happens to have a politically charged name, but the conflation you're making is not an argument, and doesn't address the underlying (inherently political) problem. > In precise terms, what do you think the problem is? The precise problem is that things like skin color etc. often have strong predictive power. We've recognized this unfortunate fact -- and its societal implications -- and decided to create laws and social norms that regulate (either restrict or sometimes even require) the use of these properties in certain high-impact decisions. Unfortunately, algorithms sometimes use these features to make decisions in settings where a typical human wouldn't (or might even get into legal hot water if they did). Notice that an algorithm can exhibit this behavior without being (statistically) biased. Now, perhaps you think this problem isn't actually a problem. And you can certainly make the argument that any form of vernacular-discrimination should be OK as long as it doesn't exhibit statistical bias. But that's a separate conversation, and you can't defend that position by playing with definitions. And if you're not making that argument, then it should be clear why vernacular-bias differs from statistical-bias.